A few previous methods proposed to tackle fast video object segmentation. In particular, FAVOS [1]} first tracks the part-based detection. Then, based on the tracked box, it generates the part-based segments and merges those parts according to a similarity score to form the final segmentation results. The drawback of FAVOS is that it can not be learned in an end-to-end manner, and heavily relies on the part-based detection performance. OSNM [2]} proposes a model which is composed of
a modulator and a segmentation network. Through encoding the mask prior, the modular can help the segmentation network quickly adapt to the target object. RGMP [3]}
shares the same spirit with OSNM. Specifically, it employs a Siamese encoder-decoder structure to utilize the mask propagation, and further boosts the performance with
synthetic data. The most similar work to ours is
PML [4]}, which formulates the problem as a pixel-wise metric learning problem. Through the FCN [5]}, it maps the pixels to high-dimensional space, and utilizes a revised triplet loss to encourage pixels belonging to the same object much closer than those belonging to different objects. Nearest neighbor (NN) is required for retrieval during inference.
In contrast our meta-learning approach
acquires a mapping matrix between the high-dimensional feature and annotated mask in reference image using ridge regression, and then can be adapted rapidly to generate the prediction mask. Compared to baseline method PML [4]}, our method achieves more accurate performance and is twice the speed. And with the same efficiency, the J mean of our method is 3.4 percent better than OSNM [2]}
on the DAVIS2016 [8]} validation set.
